Ding Zhe, Zhang Haohao, Li Hai, Chen Jinyi, Lu Ping, Hua Chao
Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.
School of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
ACS Omega. 2023 Jul 26;8(31):28487-28498. doi: 10.1021/acsomega.3c02785. eCollection 2023 Aug 8.
Reducing production costs is one of the main objectives of process intensification; in this work, production costs of the distillation process are reduced by reducing equipment size and utility consumption from the perspective of process optimization to achieve the purpose of process intensification. The application of intelligent optimization algorithms in the optimization process of distillation is vital to achieving high efficiency and low costs. Combining the harmony search algorithm with the characteristics of distillation optimization, a new distillation harmony search algorithm (DHSA) was proposed, which includes the self-adaptive adjustment of parameters, roulette selection strategy, and ratio optimization strategy. Benefiting from the DHSA, the optimal total annual cost and calculation times were remarkably reduced when compared with reported algorithms in the optimization of four distillation cases including the two-column model, three-column model, reactive distillation column model, and dividing-wall extractive distillation column model. In addition, the highest coefficient of variation of DHSA in 10 parallel calculations is 1.25%. These results indicate that DHSA has the advantages of a higher-quality solution, less computing time, and higher stability, which not only improves the optimization efficiency and quality but also inspires the optimization strategies for other algorithms.
降低生产成本是过程强化的主要目标之一；在本研究中，从过程优化的角度通过减小设备尺寸和降低公用工程消耗来降低精馏过程的生产成本，以实现过程强化的目的。智能优化算法在精馏优化过程中的应用对于实现高效低成本至关重要。结合和声搜索算法与精馏优化的特点，提出了一种新的精馏和声搜索算法（DHSA），该算法包括参数的自适应调整、轮盘赌选择策略和比例优化策略。受益于DHSA，在对包括双塔模型、三塔模型、反应精馏塔模型和隔壁萃取精馏塔模型在内的四种精馏案例进行优化时，与已报道的算法相比，最优年度总成本和计算次数显著降低。此外，DHSA在10次并行计算中的最高变异系数为1.25%。这些结果表明，DHSA具有解质量更高、计算时间更短和稳定性更高的优点，不仅提高了优化效率和质量，还为其他算法的优化策略提供了启发。
